A framework of a mechanical translation between Japanese and English by analogy principle
Proc. of the international NATO symposium on Artificial and human intelligence
Translating with Scarce Resources
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
A comparison of head transducers and transfer for a limited domain translation application
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Generation that exploits corpus-based statistical knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Char_align: a program for aligning parallel texts at the character level
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Automated generalization of translation examples
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Fast decoding and optimal decoding for machine translation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Evaluation in the ARPA machine translation program: 1993 methodology
HLT '94 Proceedings of the workshop on Human Language Technology
Crowdsourcing translation: professional quality from non-professionals
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Machine translation of human languages is a field almost as old as computers themselves. Recent approaches to this challenging problem aim at learning translation knowledge automatically (or semi-automatically) from online text corpora, especially human-translated documents. For some language pairs, substantial translation resources exist, and these corpus-based systems can perform well. But for most language pairs, data is scarce, andcurrent techniques do not work well. To examine the gap betweenhuman and machine translators, we created an experiment in which humanbeings were asked to translate an unknown language into English on thesole basis of a very small bilingual text. Participants performed quite well,and debriefings revealed a number of valuable strategies. We discuss thesestrategies and apply some of them to a statistical translation system.